Prospective comparison of the HEP score and 4Ts score for the diagnosis of heparin-induced thrombocytopenia
Bibliographic record
Abstract
Abstract The HIT Expert Probability (HEP) score compared favorably with the 4Ts score in a retrospective study. We assessed the diagnostic accuracy of the HEP score compared with the 4Ts score in a prospective cohort of 310 patients with suspected heparin-induced thrombocytopenia (HIT). A member of the clinical team calculated the HEP score and 4Ts score. An independent panel adjudicated HIT status based on a clinical summary as well as the results of HIT laboratory testing. The prevalence of HIT in the study population was 14.7%. At a cutoff of ≥3, the HEP score was 95.3% sensitive (95% confidence interval [CI], 84.2-99.4) and 35.7% specific (95% CI, 29.8-42.0) for HIT. A 4Ts score of ≥4 had a sensitivity of 97.7% (95% CI, 86.2-99.8) and specificity of 32.9% (95% CI, 27.2-39.1). The areas under the receiver operating characteristic (ROC) curves (AUCs) for the HEP score and 4Ts score were similar (0.81 [95% CI, 0.74-0.87] vs 0.76 [95% CI, 0.69-0.83]; P = .12). The HEP score exhibited a significantly higher AUC than the 4Ts score in patients in the intensive care unit (ICU) (0.86 vs 0.79; P = .03). Among trainee scorers, the HEP score performed significantly better than the 4Ts score (AUC, 0.80 vs 0.73; P = .03). Our data suggest that either the 4Ts score or the HEP score may be used in clinical practice. The HEP score may be preferable in ICU patients and among less experienced clinicians.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".